{"cells":[{"cell_type":"markdown","source":["# 1.Preparation"],"metadata":{"id":"jnIUUoqSJfcR"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21031,"status":"ok","timestamp":1731375276817,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":300},"id":"t5Nq-yJJFmCD","outputId":"b3eae0f7-7911-4600-bd59-b7f8ad420af2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2265,"status":"ok","timestamp":1731375293802,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":300},"id":"2YbSjswMFoQN","outputId":"5c0de2e7-b475-4829-a9ee-218fca7c601f"},"outputs":[{"output_type":"stream","name":"stdout","text":[" Add_Clustered_news_0.6\t\t\t\t     KO_2023-10-30_2024-09-30.json\n","'Benchmark with Llama3 8b Data.ipynb'\t\t     MCD_2023-10-30_2024-09-30.json\n"," Clustered_news_0.6\t\t\t\t    'News repetition reduction.ipynb'\n"," config.json\t\t\t\t\t     __pycache__\n"," dataset_dict.json\t\t\t\t     result.ipynb\n"," DIS_2023-10-30_2024-09-30.json\t\t\t     test\n"," finetuned_models\t\t\t\t     test_demo.ipynb\n"," fingpt-forecaster\t\t\t\t    'teststing result.docx'\n"," fingpt-forecaster-1029\t\t\t\t     train\n"," fingpt-forecaster-1102\t\t\t\t     train_lora.py\n"," fingpt-forecaster-1105\t\t\t\t     train.sh\n"," fingpt-forecaster-dow-30-20230930-20240930-1-4-06   Untitled\n"," fingpt-forecaster-dow-30-20231030-20240930-1-4-06   utils.py\n"," HD_2023-10-30_2024-09-30.json\t\t\t     wandb\n"," HON_2023-10-30_2024-09-30.json\n"]}],"source":["!ls \"/content/drive/MyDrive/Your own address\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1731375293802,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":300},"id":"FNTtNEnVFpjM","outputId":"9491328b-68ad-47c2-be55-8d16f5aa0354"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Colab Notebooks/AI4Finance/FinForecaster/Benchmark with Llama3 8b Data\n"]}],"source":["%cd \"/content/drive/MyDrive/Your own address\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":669,"status":"ok","timestamp":1731375294817,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":300},"id":"HKfJgBHGFxWt","outputId":"51402108-eb37-4c4e-84ad-e09de414e543"},"outputs":[{"output_type":"stream","name":"stdout","text":["The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n","Token is valid (permission: write).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["from huggingface_hub import login\n","\n","\n","login(token=\"Your own key\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23304,"status":"ok","timestamp":1731375318119,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":300},"id":"giYJQ0yZF4hH","outputId":"3a0ccaf9-7bf2-4fe2-bc11-be2ac38b6ce8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n","Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n","Collecting 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This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed datasets-3.1.0 deepspeed-0.15.4 dill-0.3.8 fsspec-2024.9.0 hjson-3.1.0 multiprocess-0.70.16 ninja-1.11.1.1 nvidia-ml-py-12.560.30 rouge-score-0.1.2 xxhash-3.5.0\n"]}],"source":["!pip install transformers torch datasets peft wandb deepspeed rouge-score"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jY0ZMlxJFY_f"},"outputs":[],"source":["import os\n","import torch\n","import re\n","\n","\n","\n","from transformers import AutoTokenizer, AutoModelForCausalLM\n","from peft import PeftModel\n","from datasets import load_from_disk\n","from datasets import Dataset\n","from sklearn.metrics import accuracy_score\n","from tqdm import tqdm\n","# from peft import PeftModel\n","from utils import *\n","\n"]},{"cell_type":"markdown","source":["# 2. Load model and tokenizer"],"metadata":{"id":"4m_pO-3EJjR6"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":409,"referenced_widgets":["88792a399072459d9eee1b9cb2c07ad4","689fac6940d44540aebcd21651c2baa4","450b0c0cef4e4f4db1e81649b2451962","ba7127bf67b147b590052fe6c136716e","628cd752a9ea418aa26b01eb46f408ab","0eb2c0b537ea4b6888840588e8533667","293450e47b424c7bb2b829704ae7bca8","4a5df75b65544da0a029b1c64d28f92d","e41ca5914ee246c88bcc74d85c7a7a57","25ace4f6891e4083b27e761a2a0c0c7f","90449536993742e99b8eed25a7ec7b2f","2c3c58e1052849abb62b7d6b16746f22","4ae67957027d49bc99ccd77e9ed43407","70e660999d514f4d9cafe96e26ec03f0","0dc06f38609349c7b6dddcbf631ea637","7e10cd0fc25a4e929c20963afb011594","9b300cae665d484dae202d307a6f4b4f","06c59951010843e3b47443abd9114967","1c9d22edbfd94b4389a662460af4c60a","0ab15e49cdae466fbfef99551636243e","7ffe735483c543ff83448df1b50780b5","19b8e90524ba4192a337becc76c19d7a","e89c322802b64c1fb897c9eea1b58e19","33feafacc346433789a477e1ae4a253b","0e6e831156e04e38822fabc35065d31d","eb3db62609b64f0f88f9533d4b8bfd0a","d19bcd4a7c3a4f59a0b628f0d28cf759","3e82baa8e124454c8476603fc69c3df6","d0d8c7a57ec04da4a541e7c42ab83c0d","c0c968fa9f094655ae6b27009058b494","e132be4f79b843319114bc1a2ddb357c","63d6c5b4ea6543568221c5d4462dc57e","c6ffa837ab47464ca2d84d3036be08b4","204a7ade2bda4d94bddb948afba9e90e","f67233a0089b4b0d9e686b8dce8ca2de","ceb7b15249a04a2d816f420a325503d9","ca37c2be72ac4fc4acf73269ded7fe79","235ea05c012c469ebdf85cae858613c9","e036d76ec2b6422795c2abd851ec6351","7c691fa757f24449a4ac890c2174a585","a9e1cde5430042fa8cfd3cecab78f84f","231a6133400e4608b14a9294dd1fe6b8","2153c778389c4cdb88f82c6f7bb7426b","ed79faae0c8040a78c2ccd35c1b33140","9b5ed118ba42469094468aeb7218bbad","1c7ed001e59c4e1783e1d5d13128710b","2822685991b64718a5ec2d6720cfe08f","bdc51b76351649748bb4dc849eac59be","c548e6c0649b4ecba905134169f91e59","4f25c15a78904867ab84a9aaa703aecd","0e0472ed1cab40faa60791d02d977656","a44368f5f68b4c5f8694e31d9adba07a","63c5c8dafa9e4552a965265b454b08cc","50c8c71b1474477e8dac44f8cd8327dc","c6fbe59114174b4d8078fe8f7720b8af","cb29be503f3a4f35bc0b2aa88aa4fb7a","535f991722964eeeaa7f8d2c0a822baa","89662165bc38470ea23b636fdfe56dcf","87353e5fe6904e1ca584d10296063ec3","c8f01d3e13bb4c809b5a1663a990d941","929efb181517486b8e559657e088b734","eae0721691d74dbdbfa29870a194cd24","8c0d20accde047948af826023547ca57","2e07d31d204643108789b706e6fc6cb7","8d7dc3caac6d4a4e989f0a93b4a98141","ff561ec2b0624836a3df931c2f335a70","9a5eda2a5330417597f3a9510cdc7c56","02a8478ecd80441db9fa8d8835e05432","33f36ae31df84a3ea65b0709886f558e","4652b9e932aa43e79edc29f88dcbf289","23896c24e56649939014658b1cca4986","9abfe1d1b7ec4e7f964801e0e2053414","87d82278bd9a44aa91bb34841b941055","6fd86df6ded4436c99cc6688f8ab221c","74de20b2f08b4c758d68aeae950f962b","8e2a64fbbcb74c1f9c38dd4d99b327c8","6317f4d7a4264c96b86277c2b6333970","e04f9f3e11d0429ea3c3ef8b95b452e6","ea756afeb4c049aa942a55cced5a525f","8c9700ab0bf54cd2b66737d03e8d08b5","f35bad1ce0bb4b848a9684bf213cb635","c52f057d408a44308ac78a9ce13be606","12286111f97d46c3aa60697055799791","05cdf58fe6524fd2a89b3af506a3ded7","ab30e4f848764aa087d31c813ad6b634","bb5d77ab2c244e7cb0a2ff52d523a4db","8c847fb7ac7d4fe99027969ddf1c1c31","982aa9c223d344d3b75a6764a78e982a","4365d6ddfc284b6e9e800c0fa566c40d","413bb091e65e4870bd8b456a47b50cab","d042a36d02874a9c9c445ee7666deb5c","11af4f58dbd8455e858a35b4bf12d2be","cea43bfea7b6400bb5650204f187d06d","3633c9d8a0dd4c0284c9a95950ea5f78","b14417e42cad4af0a5b7f4aa268dae26","29f4f54362964bea9c1ab0d15608bc68","1776670bc9f6400c89ed56907ac13198","beaec729204943e9b32b9aa80a481c74","602be32a1fab4ec5ba69a538dfb4fc30"]},"executionInfo":{"elapsed":397208,"status":"ok","timestamp":1731375757917,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":300},"id":"RqTZkq_vFeUe","outputId":"518d2e05-e642-4d8c-ab75-03c89c5439e6"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/654 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"88792a399072459d9eee1b9cb2c07ad4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2c3c58e1052849abb62b7d6b16746f22"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e89c322802b64c1fb897c9eea1b58e19"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"204a7ade2bda4d94bddb948afba9e90e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9b5ed118ba42469094468aeb7218bbad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00004.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cb29be503f3a4f35bc0b2aa88aa4fb7a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00004-of-00004.safetensors:   0%|          | 0.00/1.17G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9a5eda2a5330417597f3a9510cdc7c56"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e04f9f3e11d0429ea3c3ef8b95b452e6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/177 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4365d6ddfc284b6e9e800c0fa566c40d"}},"metadata":{}}],"source":["base_model = AutoModelForCausalLM.from_pretrained(\n","    'meta-llama/Meta-Llama-3-8B',\n","    trust_remote_code=True,\n","    device_map=\"auto\",\n","    torch_dtype=torch.float16,\n",")\n","base_model.model_parellal = True"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dKevgU07ItHb"},"outputs":[],"source":["model = PeftModel.from_pretrained(base_model, 'The address where your parameters are saved after your training session is complete')\n","model_hg = PeftModel.from_pretrained(base_model, 'The address where your parameters are saved after your training session is complete')\n","# Note: If you use the code we provide, the training result will generally be automatically saved in the same folder as the training colab file.\n","\n","model = model.eval()\n","model_hg = model_hg.eval()\n","base_model = base_model.eval()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["82398f80ae794ef88c1996b3294e1008","3f014a72c4f1403ea21f6f5888a78a71","ddec3f4022124434959971b935af7fbc","e5786207cd45488c81096c1b90387d26","3adbe72f2c83488e910b5e2cd962ee8c","ffe210392d8b4fbfb93560f1b88534fd","07fc416190c3428da9680bd306d24815","97ac2a32c5c8474e91ac40856cfff4d5","88b087038bfa4ced9642035bea117f75","911a8fd2ebf74a4188f9ee7ee9a3ebd7","c9ad00dbf6794c0a9d923549edfba10c","77339e289db34d0fb8c6a30a9c8bfbf8","f88c025800ac465a96de27ba7753ce1b","7aeb3b48619d40fe893d512373219811","726f2c5b0d04419d8461539b10c61b59","34f3e7f6066c4c87ad3f422ff9271a9e","ecd3266ac12e4e96993c5d55e530db3f","50ceb5e37a14475fa77b7ae59a091e25","5ccc7519c3b344aa9b2ba7ab47337f7d","5663f067943549b49ba4b3d4a6ea2b09","515c370bf94e4eb199579757ba756037","812e7ad1eddd40b4bfd515580c8d5bce","753aa7d758634a2189047a7a94c65748","dd5725515b274d449be70bd33de7e945","67b0512e864a447b8b95a9e2dda51968","bba074dc3bf04a518e80a23c92db9cfb","cfc482b875e44464b89025c802ab6123","9d0e8cb3a2a3441780ad37e9b0b3b23f","7c077d0d6f7641839f581723e29cb6ff","fbde7e29b9ad4065a74e64e254f1540b","6d0a40b1f9724a65b041241af877938c","9d2a2d899b704de585e24f79fff37459","c0eaba9cbbe0401ab8fe0dbe39583ac4"]},"executionInfo":{"elapsed":4020,"status":"ok","timestamp":1731375780135,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":300},"id":"BqJKNdrQJXxi","outputId":"b709254a-f3d3-41b3-a94b-4776076c41b0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"82398f80ae794ef88c1996b3294e1008"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"77339e289db34d0fb8c6a30a9c8bfbf8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/73.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"753aa7d758634a2189047a7a94c65748"}},"metadata":{}}],"source":["\n","tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B')\n","\n","\n","tokenizer.padding_side = \"right\"\n","\n","\n","tokenizer.pad_token_id = tokenizer.eos_token_id"]},{"cell_type":"markdown","source":["# 3. Load the test set"],"metadata":{"id":"cuIF2WtDJlyh"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"7TtTqv4JOpVZ"},"outputs":[],"source":["\n","test_dataset = load_from_disk('/content/drive/MyDrive/Your test set address')"]},{"cell_type":"code","source":["test_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FGj9NjEXl9Nk","executionInfo":{"status":"ok","timestamp":1731375354985,"user_tz":300,"elapsed":3,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"3f4e4d25-0cf7-4c9b-c133-038a7709c7c5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['prompt', 'answer', 'period', 'label', 'symbol'],\n","    num_rows: 530\n","})"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["If you want to test on a company basis or want to see how the model predicts the stock price of different companies, you can use the following codes to filter the test set based on the stock code."],"metadata":{"id":"cGHFBzbxG5Qh"}},{"cell_type":"code","source":["def filter_by_ticker(test_dataset, ticker_code):\n","\n","    filtered_data = []\n","\n","    for row in test_dataset:\n","        prompt_content = row['prompt']\n","\n","        ticker_symbol = re.search(r\"ticker\\s([A-Z]+)\", prompt_content)\n","\n","        if ticker_symbol and ticker_symbol.group(1) == ticker_code:\n","            filtered_data.append(row)\n","\n","    filtered_dataset = Dataset.from_dict({key: [row[key] for row in filtered_data] for key in test_dataset.column_names})\n","\n","    return filtered_dataset\n","\n"],"metadata":{"id":"M-h5w0p4KrW6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def get_unique_ticker_symbols(test_dataset):\n","\n","    ticker_symbols = set()\n","\n","    for i in range(len(test_dataset)):\n","        prompt_content = test_dataset[i]['prompt']\n","\n","        ticker_symbol = re.search(r\"ticker\\s([A-Z]+)\", prompt_content)\n","\n","        if ticker_symbol:\n","            ticker_symbols.add(ticker_symbol.group(1))\n","\n","    return list(ticker_symbols)\n","\n","\n","\n","def insert_guidance_after_intro(prompt):\n","\n","    intro_marker = (\n","        \"[INST]<<SYS>>\\n\"\n","        \"You are a seasoned stock market analyst. Your task is to list the positive developments and \"\n","        \"potential concerns for companies based on relevant news and basic financials from the past weeks, \"\n","        \"then provide an analysis and prediction for the companies' stock price movement for the upcoming week.\"\n","    )\n","    guidance_start_marker = \"Based on all the information before\"\n","    guidance_end_marker = \"Following these instructions, please come up with 2-4 most important positive factors\"\n","\n","    intro_pos = prompt.find(intro_marker)\n","    guidance_start_pos = prompt.find(guidance_start_marker)\n","    guidance_end_pos = prompt.find(guidance_end_marker)\n","\n","    if intro_pos == -1 or guidance_start_pos == -1 or guidance_end_pos == -1:\n","        return prompt\n","\n","    guidance_section = prompt[guidance_start_pos:guidance_end_pos].strip()\n","\n","    new_prompt = (\n","        f\"{prompt[:intro_pos + len(intro_marker)]}\\n\\n\"\n","        f\"{guidance_section}\\n\\n\"\n","        f\"{prompt[intro_pos + len(intro_marker):guidance_start_pos]}\"\n","        f\"{prompt[guidance_end_pos:]}\"\n","    )\n","\n","    return new_prompt\n","\n","\n","def apply_to_all_prompts_in_dataset(test_dataset):\n","\n","    updated_dataset = test_dataset.map(lambda x: {\"prompt\": insert_guidance_after_intro(x[\"prompt\"])})\n","\n","    return updated_dataset"],"metadata":{"id":"HduF1LzJnT7o"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_dataset = apply_to_all_prompts_in_dataset(test_dataset)"],"metadata":{"id":"fO_RoKrwg7XI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","unique_symbols = set(test_dataset['symbol'])\n","\n","print(f\"Total number of unique stock codes: {len(unique_symbols)}\")\n","\n","print(f\"Unique stock codes: {unique_symbols}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I6Xk_D_RhAGn","executionInfo":{"status":"ok","timestamp":1730946149912,"user_tz":300,"elapsed":2,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"af8ae422-20b2-42c1-9b2c-3f1d4fbbe8ec"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Total number of unique stock codes: 30\n","Unique stock codes: {'DIS', 'WMT', 'TRV', 'KO', 'CSCO', 'GS', 'AXP', 'MCD', 'CRM', 'MSFT', 'JNJ', 'MRK', 'PG', 'JPM', 'DOW', 'HON', 'VZ', 'CAT', 'UNH', 'BA', 'NKE', 'INTC', 'AMGN', 'CVX', 'MMM', 'AAPL', 'IBM', 'HD', 'WBA', 'V'}\n"]}]},{"cell_type":"code","source":["test_dataset_AXP['symbol'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"87PxSUIR9iQx","executionInfo":{"status":"ok","timestamp":1730603265664,"user_tz":240,"elapsed":138,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"a63aa8fb-2d11-49ff-a43a-22eb540c924a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'AXP'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["test_dataset_AAPL = filter_by_ticker(test_dataset, \"AAPL\")\n","print(test_dataset_AAPL)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Jqtn63WqODrW","executionInfo":{"status":"ok","timestamp":1730603266758,"user_tz":240,"elapsed":143,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"ed3a59d6-aa1d-4089-8e57-0bab901d27f2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['prompt', 'answer', 'period', 'label', 'symbol'],\n","    num_rows: 19\n","})\n"]}]},{"cell_type":"code","source":["test_dataset_AMGN = filter_by_ticker(test_dataset, \"AMGN\")\n","print(test_dataset_AMGN)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5GHGD37Lo7EH","executionInfo":{"status":"ok","timestamp":1730603270203,"user_tz":240,"elapsed":132,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"88b68e63-a198-4381-ad36-c1e1073627ae"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['prompt', 'answer', 'period', 'label', 'symbol'],\n","    num_rows: 19\n","})\n"]}]},{"cell_type":"code","source":["test_dataset_AMGN['prompt'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145},"id":"YW2NrXYznn8n","executionInfo":{"status":"ok","timestamp":1730603271253,"user_tz":240,"elapsed":197,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"512b41d3-8abf-449b-c5f3-d8b509b32ff8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"[INST]<<SYS>>\\nYou are a seasoned stock market analyst. Your task is to list the positive developments and potential concerns for companies based on relevant news and basic financials from the past weeks, then provide an analysis and prediction for the companies' stock price movement for the upcoming week. Your answer format should be as follows:\\n\\n[Positive Developments]:\\n1. ...\\n\\n[Potential Concerns]:\\n1. ...\\n\\n[Prediction & Analysis]\\nPrediction: ...\\nAnalysis: ...\\n\\n<</SYS>>\\n\\n[Company Introduction]:\\n\\nAmgen Inc is a leading entity in the Biotechnology sector. Incorporated and publicly traded since 1983-06-17, the company has established its reputation as one of the key players in the market. As of today, Amgen Inc has a market capitalization of 171526.24 in USD, with 537.33 shares outstanding.\\n\\nAmgen Inc operates primarily in the US, trading under the ticker AMGN on the NASDAQ NMS - GLOBAL MARKET. As a dominant force in the Biotechnology space, the company continues to innovate and drive progress within the industry.\\n\\nFrom 2024-05-19 to 2024-05-26, AMGN's stock price decreased from 310.31 to 303.73. The stock price each day during this period is [312.365, 312.673, 307.271, 304.381, 303.725], and the corresponding daily return is given by [0.0066, 0.001, -0.0173, -0.0094, -0.0022]. News during this period are listed below:\\n\\n[Topic]: The Zacks Analyst Blog Highlights Apple, Mastercard, Amgen, Utah Medical Products and Bridger Aerospace\\n[Topic Duration]:2024-05-19 to 2024-05-24\\n[News Headline]:The Zacks Analyst Blog Highlights Apple, Mastercard, Amgen, Utah Medical Products and Bridger Aerospace\\n[News Summary]: Apple, Mastercard, Amgen, Utah Medical Products and Bridger Aerospace are part of the Zacks top Analyst Blog.\\n[Topic Size]:2\\n\\n[Topic]: Amgen, Inc. (AMGN) ATS and the Approval of IMDELLTRA Conference - (Transcript)\\n[Topic Duration]:2024-05-19 to 2024-05-26\\n[News Headline]:Amgen, Inc. (AMGN) ATS and the Approval of IMDELLTRA Conference - (Transcript)\\n[News Summary]: Amgen, Inc. (NASDAQ:NASDAQ:AMGN) ATS and the Approval of IMDELLTRA Conference Call May 20, 2024 4:00 PM ETCompany ParticipantsJustin Claeys - Vice President...\\n[Topic Size]:2\\n\\n[Topic]: 7 Biotech Stocks That Could Breathe New Life Into Your Portfolio\\n[Topic Duration]:2024-05-20 to 2024-05-21\\n[News Headline]:7 Biotech Stocks That Could Breathe New Life Into Your Portfolio\\n[News Summary]: Fundamentally, one of the biggest advantages of biotech stocks to buy centers on the underlying prioritization. While the media focuses on attaining wealth, that doesn’t mean much if you don’t have health. Therefore, this compelling subcategory of the broader healthcare market deserves attention. In addition, while the market has been robust – with the Dow Jones Industrial Average breaching the 40,000-point level – many concerns exist. Inflation remains elevated as are borrowing costs. Delinquen\\n[Topic Size]:2\\n\\n[Basic Financials]:\\n\\nNo basic financial reported.\\n\\nBased on all the information before 2024-05-26 and the following guidelines, let's analyze the positive developments and potential concerns for AAPL. Apart from stock price information and basic financials, several news topcis are given and they are derived from news clustering. Each topic contains its duration, a key news headline and summary, and a topic size, indicating the number of related news items within that cluster. First, note that some of the factors only influence the price for the following 1 or 2 days while some others may have longer-term effects. Second, the topics that are closer to 2023-11-12 are likely to have a stronger influence on the upcoming stock price forecast. Third, take into account the topic size: larger clusters represent greater market attention and, consequently, likely exert more influence on the stock price. Following these instructions, please come up with 2-4 most important positive factors and 2-4 most significant negative effects and keep them concise. Most factors should be inferred from company related news.Then make your prediction of the AMGN price movement for next week (2024-05-26 to 2024-06-02). Provide a summary analysis to support your prediction.[/INST]\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":26}]},{"cell_type":"code","source":["test_dataset_AXP_new = apply_to_all_prompts_in_dataset(test_dataset_AXP)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["b79d02882132416a81be90e21994a46b","0c0b089be9fe4822ad46a15eb9d1f4e4","a89fc3cec496482dadfb97c46e6f7882","dbf1967891a7496abe04e6845dd02c85","c993033e215945339c837fe6612edf07","a178656d66e64bd591962440072613a5","dbc5ef2fbbd94034981723ddfc5dcab5","3a57a7d35af046bc8e81ef2afbed5d96","27a5420ab2584f32a54864b33e60d8cb","3c42336064514737a224c862b5b10bd8","ef8146092d0a41f6922cfe11e356ba56"]},"id":"wRXWYfjSXLCA","executionInfo":{"status":"ok","timestamp":1730603272572,"user_tz":240,"elapsed":144,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"c9c3825f-7b2a-475b-d99b-b65f31c68b8c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/19 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b79d02882132416a81be90e21994a46b"}},"metadata":{}}]},{"cell_type":"code","source":["test_dataset_AAPL_new = apply_to_all_prompts_in_dataset(test_dataset_AAPL)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["063e745237f34e19a006c452f6c50f9f","a98a25637e9e4811ab2df748248086bb","67318a3e0f8141d8bb3d6397f16a2031","ab218f9ac938478a8dd99a769a5949c5","b7225c7044dd4d30a172f2a3a3823ad0","600f6267381446ffb7ac0b9498dff04e","9d34d094b9e14ec9b196ee3b1f67e191","87e344a2dc584f2095e605201a800a1a","bb7b985051cd49f58e6fac80544da907","9d57ead378ea4f118fab8900a695e32c","5b044e3418f049a8b00f8a38d41c5dba"]},"id":"m-JYPXSpcZ3k","executionInfo":{"status":"ok","timestamp":1730603273736,"user_tz":240,"elapsed":130,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"f92ff80d-398a-4161-fd8c-3ee0cdb9e779"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/19 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"063e745237f34e19a006c452f6c50f9f"}},"metadata":{}}]},{"cell_type":"code","source":["test_dataset_AMGN_new = apply_to_all_prompts_in_dataset(test_dataset_AMGN)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["fa0aabef62b74c54bb291b028c8bc462","46d3a26d95ad4473b5790e50e52f1f39","88c42e678b614a61b14ea3a01a9ca741","78b101be3c1a473ea1fbcb197047f23e","829d6f5496754e8e86bdcd47a4895184","ba9857419def4368b7b167aa18ca1b45","b24fd7d2581a4c9db371fcc1296a884f","d4beef38bc2c46c19dc56f53a6672759","3316dbbb4a0942feb0aac87c31fdb48b","7f4e1443abb848a0971bbae7c8ec855b","32e2deb784aa45b5adb76fdcb3b0f358"]},"id":"XuEPROX2cc8o","executionInfo":{"status":"ok","timestamp":1730603274694,"user_tz":240,"elapsed":125,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"b33cd2e1-b411-430c-9b41-fc65fdd9a3ac"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/19 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa0aabef62b74c54bb291b028c8bc462"}},"metadata":{}}]},{"cell_type":"code","source":["test_dataset_AMGN_new['prompt'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":547},"id":"r_5nCqcNoAbw","executionInfo":{"status":"ok","timestamp":1730603275464,"user_tz":240,"elapsed":109,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"b3e70066-b545-4854-a438-27dac8298504"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"[INST]<<SYS>>\\nYou are a seasoned stock market analyst. Your task is to list the positive developments and potential concerns for companies based on relevant news and basic financials from the past weeks, then provide an analysis and prediction for the companies' stock price movement for the upcoming week.\\n\\nBased on all the information before 2024-05-26 and the following guidelines, let's analyze the positive developments and potential concerns for AAPL. Apart from stock price information and basic financials, several news topcis are given and they are derived from news clustering. Each topic contains its duration, a key news headline and summary, and a topic size, indicating the number of related news items within that cluster. First, note that some of the factors only influence the price for the following 1 or 2 days while some others may have longer-term effects. Second, the topics that are closer to 2023-11-12 are likely to have a stronger influence on the upcoming stock price forecast. Third, take into account the topic size: larger clusters represent greater market attention and, consequently, likely exert more influence on the stock price.\\n\\n Your answer format should be as follows:\\n\\n[Positive Developments]:\\n1. ...\\n\\n[Potential Concerns]:\\n1. ...\\n\\n[Prediction & Analysis]\\nPrediction: ...\\nAnalysis: ...\\n\\n<</SYS>>\\n\\n[Company Introduction]:\\n\\nAmgen Inc is a leading entity in the Biotechnology sector. Incorporated and publicly traded since 1983-06-17, the company has established its reputation as one of the key players in the market. As of today, Amgen Inc has a market capitalization of 171526.24 in USD, with 537.33 shares outstanding.\\n\\nAmgen Inc operates primarily in the US, trading under the ticker AMGN on the NASDAQ NMS - GLOBAL MARKET. As a dominant force in the Biotechnology space, the company continues to innovate and drive progress within the industry.\\n\\nFrom 2024-05-19 to 2024-05-26, AMGN's stock price decreased from 310.31 to 303.73. The stock price each day during this period is [312.365, 312.673, 307.271, 304.381, 303.725], and the corresponding daily return is given by [0.0066, 0.001, -0.0173, -0.0094, -0.0022]. News during this period are listed below:\\n\\n[Topic]: The Zacks Analyst Blog Highlights Apple, Mastercard, Amgen, Utah Medical Products and Bridger Aerospace\\n[Topic Duration]:2024-05-19 to 2024-05-24\\n[News Headline]:The Zacks Analyst Blog Highlights Apple, Mastercard, Amgen, Utah Medical Products and Bridger Aerospace\\n[News Summary]: Apple, Mastercard, Amgen, Utah Medical Products and Bridger Aerospace are part of the Zacks top Analyst Blog.\\n[Topic Size]:2\\n\\n[Topic]: Amgen, Inc. (AMGN) ATS and the Approval of IMDELLTRA Conference - (Transcript)\\n[Topic Duration]:2024-05-19 to 2024-05-26\\n[News Headline]:Amgen, Inc. (AMGN) ATS and the Approval of IMDELLTRA Conference - (Transcript)\\n[News Summary]: Amgen, Inc. (NASDAQ:NASDAQ:AMGN) ATS and the Approval of IMDELLTRA Conference Call May 20, 2024 4:00 PM ETCompany ParticipantsJustin Claeys - Vice President...\\n[Topic Size]:2\\n\\n[Topic]: 7 Biotech Stocks That Could Breathe New Life Into Your Portfolio\\n[Topic Duration]:2024-05-20 to 2024-05-21\\n[News Headline]:7 Biotech Stocks That Could Breathe New Life Into Your Portfolio\\n[News Summary]: Fundamentally, one of the biggest advantages of biotech stocks to buy centers on the underlying prioritization. While the media focuses on attaining wealth, that doesn’t mean much if you don’t have health. Therefore, this compelling subcategory of the broader healthcare market deserves attention. In addition, while the market has been robust – with the Dow Jones Industrial Average breaching the 40,000-point level – many concerns exist. Inflation remains elevated as are borrowing costs. Delinquen\\n[Topic Size]:2\\n\\n[Basic Financials]:\\n\\nNo basic financial reported.\\n\\nFollowing these instructions, please come up with 2-4 most important positive factors and 2-4 most significant negative effects and keep them concise. Most factors should be inferred from company related news.Then make your prediction of the AMGN price movement for next week (2024-05-26 to 2024-06-02). Provide a summary analysis to support your prediction.[/INST]\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":30}]},{"cell_type":"markdown","source":["# 4.Testing"],"metadata":{"id":"aqxJtEEgOJgZ"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"vCMaruzhOp35"},"outputs":[],"source":["def test_demo(model, tokenizer, prompt):\n","\n","    inputs = tokenizer(\n","        prompt, return_tensors='pt',\n","        padding=False, max_length=8000\n","    )\n","    inputs = {key: value.to(model.device) for key, value in inputs.items()}\n","\n","    res = model.generate(\n","        **inputs, max_length=4096, do_sample=True,\n","        eos_token_id=tokenizer.eos_token_id,\n","        use_cache=True\n","    )\n","    output = tokenizer.decode(res[0], skip_special_tokens=True)\n","    return output\n","    # return res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"07Gs-sWHO5ne","colab":{"base_uri":"https://localhost:8080/","height":446},"executionInfo":{"status":"error","timestamp":1730662413273,"user_tz":300,"elapsed":13711,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"0d803656-ec8a-4c85-a5f8-5d01c4c370f7"},"outputs":[{"output_type":"stream","name":"stderr","text":["\rProcessing test samples:   0%|          | 0/570 [00:00<?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:   0%|          | 0/570 [00:13<?, ?it/s]\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-16-3096f7e39ef0>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;31m# 使用基础模型 base_model 生成结果\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0moutput_base\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_demo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0manswer_base\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'.*\\[/INST\\]\\s*'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_base\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDOTALL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-15-c74b784e6a45>\u001b[0m in \u001b[0;36mtest_demo\u001b[0;34m(model, tokenizer, prompt)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     res = model.generate(\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4096\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_sample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0meos_token_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meos_token_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   2022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2023\u001b[0m             \u001b[0;31m# 13. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2024\u001b[0;31m             result = self._sample(\n\u001b[0m\u001b[1;32m   2025\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2026\u001b[0m                 \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprepared_logits_processor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36m_sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, logits_warper, **model_kwargs)\u001b[0m\n\u001b[1;32m   2980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2981\u001b[0m             \u001b[0;31m# forward pass to get next token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2982\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2983\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2984\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msynced_gpus\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mthis_peer_finished\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in 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1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m 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self.model(\n\u001b[0m\u001b[1;32m   1190\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: 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input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, cache_position)\u001b[0m\n\u001b[1;32m    999\u001b[0m                 )\n\u001b[1;32m   1000\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1001\u001b[0;31m                 layer_outputs = decoder_layer(\n\u001b[0m\u001b[1;32m   1002\u001b[0m                     \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m                     \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcausal_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   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torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m 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code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/dropout.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mdropout\u001b[0;34m(input, p, training, inplace)\u001b[0m\n\u001b[1;32m   1423\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"dropout probability has to be between 0 and 1, but got {p}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1424\u001b[0m     return (\n\u001b[0;32m-> 1425\u001b[0;31m         \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1426\u001b[0m     )\n\u001b[1;32m   1427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["from sklearn.metrics import accuracy_score\n","import re\n","from tqdm import tqdm\n","\n","\n","answers_base, answers_fine_tuned, gts = [], [], []\n","\n","for i in tqdm(range(len(test_dataset)), desc=\"Processing test samples\"):\n","    try:\n","        prompt = test_dataset[i]['prompt']\n","        gt = test_dataset[i]['answer']\n","\n","        output_base = test_demo(base_model, tokenizer, prompt)\n","        answer_base = re.sub(r'.*\\[/INST\\]\\s*', '', output_base, flags=re.DOTALL)\n","\n","        output_fine_tuned = test_demo(model, tokenizer, prompt)\n","        answer_fine_tuned = re.sub(r'.*\\[/INST\\]\\s*', '', output_fine_tuned, flags=re.DOTALL)\n","\n","        answers_base.append(answer_base)\n","        answers_fine_tuned.append(answer_fine_tuned)\n","        gts.append(gt)\n","\n","    except Exception as e:\n","        print(f\"Error processing sample {i}: {e}\")"]},{"cell_type":"markdown","source":["This function is used to evaluate the predictive accuracy of a model for all data in the test set."],"metadata":{"id":"C98Zcc9IHpgU"}},{"cell_type":"code","source":["def test_acc(test_dataset):\n","  answers_base, answers_fine_tuned, gts = [], [], []\n","\n","  for i in tqdm(range(len(test_dataset)), desc=\"Processing test samples\"):\n","      try:\n","          prompt = test_dataset[i]['prompt']\n","          gt = test_dataset[i]['answer']\n","\n","          output_base = test_demo(base_model, tokenizer, prompt)\n","          answer_base = re.sub(r'.*\\[/INST\\]\\s*', '', output_base, flags=re.DOTALL)\n","\n","          output_fine_tuned = test_demo(model, tokenizer, prompt)\n","          answer_fine_tuned = re.sub(r'.*\\[/INST\\]\\s*', '', output_fine_tuned, flags=re.DOTALL)\n","\n","          answers_base.append(answer_base)\n","          answers_fine_tuned.append(answer_fine_tuned)\n","          gts.append(gt)\n","\n","      except Exception as e:\n","          print(f\"Error processing sample {i}: {e}\")\n","  return answers_base, answers_fine_tuned,gts"],"metadata":{"id":"n9PMT-ddQZFw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["This function is used to evaluate the model's prediction results for data from a specific company (symbol)."],"metadata":{"id":"8hZ7ADElHxmj"}},{"cell_type":"code","source":["def calculate_acc_per_symbol(test_dataset, base_model, fine_tuned_model, tokenizer):\n","    symbol_metrics = {}\n","    unique_symbols = set(test_dataset['symbol'])\n","\n","\n","    for symbol in unique_symbols:\n","        print(f\"Processing symbol: {symbol}\")\n","\n","\n","        symbol_data = test_dataset.filter(lambda x: x['symbol'] == symbol)\n","\n","        answers_base, answers_fine_tuned, gts = [], [], []\n","\n","\n","        for i in tqdm(range(len(symbol_data)), desc=f\"Processing samples for {symbol}\"):\n","            try:\n","                prompt = symbol_data[i]['prompt']\n","                gt = symbol_data[i]['answer']\n","\n","\n","                output_base = test_demo(base_model, tokenizer, prompt)\n","                answer_base = re.sub(r'.*\\[/INST\\]\\s*', '', output_base, flags=re.DOTALL)\n","\n","                output_fine_tuned = test_demo(fine_tuned_model, tokenizer, prompt)\n","                answer_fine_tuned = re.sub(r'.*\\[/INST\\]\\s*', '', output_fine_tuned, flags=re.DOTALL)\n","\n","                answers_base.append(answer_base)\n","                answers_fine_tuned.append(answer_fine_tuned)\n","                gts.append(gt)\n","\n","            except Exception as e:\n","                print(f\"Error processing sample {i} for symbol {symbol}: {e}\")\n","\n","\n","        metrics_base = calc_metrics(answers_base, gts)\n","        metrics_fine_tuned = calc_metrics(answers_fine_tuned, gts)\n","\n","\n","        symbol_metrics[symbol] = {\n","            \"base_model_metrics\": metrics_base,\n","            \"fine_tuned_model_metrics\": metrics_fine_tuned\n","        }\n","\n","    return symbol_metrics\n"],"metadata":{"id":"CsP6M-0XfmWI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["AMD:\n","\n","Binary Accuracy: 0.50  |  Mean Square Error: 39.61\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5060177553893659, 'rouge2': 0.1741811068503716, 'rougeL': 0.2980045119994417}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4320301010968262, 'rouge2': 0.11705631429903035, 'rougeL': 0.23476935143404398}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.470459778622912, 'rouge2': 0.14549648189945077, 'rougeL': 0.23199012435457333}\n","\n","Binary Accuracy: 0.56  |  Mean Square Error: 36.89\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5013257403701984, 'rouge2': 0.17160271287637208, 'rougeL': 0.2765109599822412}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.42679645776577774, 'rouge2': 0.11955874122187964, 'rougeL': 0.2439839872312159}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.49239721054961194, 'rouge2': 0.1491389887533065, 'rougeL': 0.24172794173827286}\n","\n","\n","\n","TSLA:\n","\n","Binary Accuracy: 0.53  |  Mean Square Error: 23.89\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.4955854560308768, 'rouge2': 0.18580716071155987, 'rougeL': 0.30459609561463113}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.45721507013860124, 'rouge2': 0.16883641324246051, 'rougeL': 0.28556722767739756}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4691666536421016, 'rouge2': 0.13760697116145462, 'rougeL': 0.2349413533316067}\n","\n","Binary Accuracy: 0.58  |  Mean Square Error: 22.58\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.4758367091365868, 'rouge2': 0.16566794148490854, 'rougeL': 0.2857270432003906}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.44977872673340447, 'rouge2': 0.16485333744699074, 'rougeL': 0.29809084649515283}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.47395686964563516, 'rouge2': 0.1393596974109519, 'rougeL': 0.23429647472672885}\n","\n","\n","CEG:\n","\n","Binary Accuracy: 0.56  |  Mean Square Error: 42.44\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5108622631717826, 'rouge2': 0.21790882997716549, 'rougeL': 0.32972183999689525}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.42048202209863894, 'rouge2': 0.12751400507216892, 'rougeL': 0.24934096282510254}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4583018775461255, 'rouge2': 0.1401242211215103, 'rougeL': 0.22690845487821173}\n","\n","Binary Accuracy: 0.59  |  Mean Square Error: 36.94\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5235472528020955, 'rouge2': 0.2005736510145733, 'rougeL': 0.31953434551717946}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4525749607991249, 'rouge2': 0.13527392723109263, 'rougeL': 0.26175262148128187}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4886748580080039, 'rouge2': 0.15530722436934705, 'rougeL': 0.236985159361098}\n","\n","\n","\n","CRWD:\n","\n","Binary Accuracy: 0.50  |  Mean Square Error: 41.67\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.48155046619540876, 'rouge2': 0.16640213285548217, 'rougeL': 0.2992946369735689}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4328355113217057, 'rouge2': 0.14135312190678792, 'rougeL': 0.2775081856613268}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4728548982525917, 'rouge2': 0.14080500004119317, 'rougeL': 0.23896424439091943}\n","\n","Binary Accuracy: 0.47  |  Mean Square Error: 39.95\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.4727759985491417, 'rouge2': 0.17148213489822542, 'rougeL': 0.30628217430087534}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4467224696542352, 'rouge2': 0.14804014578465055, 'rougeL': 0.27756822386985275}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.48301158845346737, 'rouge2': 0.15113079450489178, 'rougeL': 0.23670955346412748}\n","\n","\n","XEL:\n","\n","Binary Accuracy: 0.56  |  Mean Square Error: 4.89\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5064365480332333, 'rouge2': 0.19608131212548877, 'rougeL': 0.3070568765978633}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4109001753037815, 'rouge2': 0.11984506983524759, 'rougeL': 0.26367335406795406}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.46377798059585285, 'rouge2': 0.13651898628351553, 'rougeL': 0.2466859671025123}\n","\n","Binary Accuracy: 0.74  |  Mean Square Error: 4.84\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.4928056499614663, 'rouge2': 0.18805595912689962, 'rougeL': 0.30947819127901494}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.40990241297484037, 'rouge2': 0.12655796340799857, 'rougeL': 0.24277686419790556}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.48574559901416486, 'rouge2': 0.15003926087079708, 'rougeL': 0.2444016392208824}\n","\n","\n","VST:\n","\n","Binary Accuracy: 0.61  |  Mean Square Error: 22.83\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5192480372889094, 'rouge2': 0.19681931999911356, 'rougeL': 0.31764684034235224}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.43365789936239474, 'rouge2': 0.1364632945334866, 'rougeL': 0.25635318043876015}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4806898829238357, 'rouge2': 0.1408750143948886, 'rougeL': 0.23431468225791935}\n","\n","Binary Accuracy: 0.59  |  Mean Square Error: 35.24\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5198402415499522, 'rouge2': 0.1994169563022763, 'rougeL': 0.3033667887470872}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.44289668743813726, 'rouge2': 0.14372259756758426, 'rougeL': 0.2752725647956803}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4748963937159137, 'rouge2': 0.14385664082196298, 'rougeL': 0.23608888544912704}\n","\n","\n","GOOG:\n","\n","Binary Accuracy: 0.53  |  Mean Square Error: 11.68\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.4840547357914029, 'rouge2': 0.16926713317343478, 'rougeL': 0.29431028379721835}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.44005460980013716, 'rouge2': 0.1413277244534965, 'rougeL': 0.270330232317154}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.47461510368805493, 'rouge2': 0.14169379373024102, 'rougeL': 0.24057318912931852}\n","\n","Binary Accuracy: 0.61  |  Mean Square Error: 10.50\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.49731909117373324, 'rouge2': 0.17842148203281083, 'rougeL': 0.3131729542543711}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4422309289628772, 'rouge2': 0.15629771070656226, 'rougeL': 0.2797110771397098}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.45587751891005257, 'rouge2': 0.13374550432378426, 'rougeL': 0.22164697541439782}\n","\n","\n","\n","AAPL:\n","\n","Binary Accuracy: 0.79  |  Mean Square Error: 18.11\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5074943278107572, 'rouge2': 0.1795394134223658, 'rougeL': 0.3047268587330497}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4700635118741938, 'rouge2': 0.1514768706432656, 'rougeL': 0.2787390782259439}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4615849531647695, 'rouge2': 0.14103830669059378, 'rougeL': 0.23395830867170173}\n","\n","Binary Accuracy: 0.63  |  Mean Square Error: 19.95\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5113683307401696, 'rouge2': 0.1966516233909619, 'rougeL': 0.3186625206074603}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4813836391347546, 'rouge2': 0.16275360313031614, 'rougeL': 0.29822886076960264}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.46538859569893193, 'rouge2': 0.13071417024354628, 'rougeL': 0.23022148689089084}\n","\n","\n","VZ:\n","\n","Binary Accuracy: 0.63  |  Mean Square Error: 8.21\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5541870488132258, 'rouge2': 0.24687024062470897, 'rougeL': 0.38322924113978335}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4191479511153993, 'rouge2': 0.1335303509781738, 'rougeL': 0.25903057741311164}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.47521650171098184, 'rouge2': 0.14920375476411704, 'rougeL': 0.24864026246778095}\n","\n","Binary Accuracy: 0.61  |  Mean Square Error: 8.22\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5360594178134275, 'rouge2': 0.21601113054296522, 'rougeL': 0.3349334592033867}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4348214942419463, 'rouge2': 0.14137369184726012, 'rougeL': 0.28181909577906583}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4602646401045971, 'rouge2': 0.13272675334469491, 'rougeL': 0.2197110031245989}\n","\n","\n","INTC:\n","\n","\n","Binary Accuracy: 0.42  |  Mean Square Error: 34.47\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5044366137949622, 'rouge2': 0.19472265558359955, 'rougeL': 0.3168994473772751}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4384399454673387, 'rouge2': 0.12502801663887333, 'rougeL': 0.2579521552333636}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.44826143394766843, 'rouge2': 0.12930680885241969, 'rougeL': 0.2295481159513373}\n","\n","Binary Accuracy: 0.42  |  Mean Square Error: 37.53\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5123696956778591, 'rouge2': 0.1834028806034139, 'rougeL': 0.3011073547976688}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.43267917522601007, 'rouge2': 0.1231402962933088, 'rougeL': 0.2533042863123596}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.46644929098514637, 'rouge2': 0.13671058812005013, 'rougeL': 0.22050506934872763}\n","\n","\n","AVGO:\n","\n","Binary Accuracy: 0.39  |  Mean Square Error: 39.39\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5051663103320295, 'rouge2': 0.1954931131036871, 'rougeL': 0.31404490188844253}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4034919199773857, 'rouge2': 0.11701674800201098, 'rougeL': 0.24856414863790516}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4855187933796391, 'rouge2': 0.15018704129157406, 'rougeL': 0.2384208014868121}\n","\n","Binary Accuracy: 0.42  |  Mean Square Error: 31.00\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5161911854377392, 'rouge2': 0.19329678807852307, 'rougeL': 0.3174628734723166}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.3898818438226846, 'rouge2': 0.09656287647209671, 'rougeL': 0.20843759726729894}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.47232512718053765, 'rouge2': 0.13426963230815145, 'rougeL': 0.23757761368631763}\n","\n","\n","BA:\n","\n","Binary Accuracy: 0.44  |  Mean Square Error: 30.81\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5035205412609165, 'rouge2': 0.22604483110252127, 'rougeL': 0.34739719752389064}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.47292023886176965, 'rouge2': 0.16013181111462055, 'rougeL': 0.27682596956838246}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.450436613320355, 'rouge2': 0.13184015629346243, 'rougeL': 0.23837404695384337}\n","\n","Binary Accuracy: 0.62  |  Mean Square Error: 21.31\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.49224906197048685, 'rouge2': 0.2046966846977495, 'rougeL': 0.32660609913299354}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.47667496340605314, 'rouge2': 0.16941686801384065, 'rougeL': 0.29870395765383967}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4586082863311141, 'rouge2': 0.12989634871606792, 'rougeL': 0.24085422436245105}\n","\n","\n","VKTX:\n","\n","Binary Accuracy: 0.56  |  Mean Square Error: 32.00\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5146670548959649, 'rouge2': 0.23075689193195392, 'rougeL': 0.3288873142784038}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4370243345723306, 'rouge2': 0.14456637347580437, 'rougeL': 0.2851113631065537}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4670337874961807, 'rouge2': 0.14326068992483815, 'rougeL': 0.2350062437595016}\n","\n","Binary Accuracy: 0.58  |  Mean Square Error: 36.68\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5245794862742773, 'rouge2': 0.2285185805821582, 'rougeL': 0.3369880670987263}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.44811422400364676, 'rouge2': 0.1567177569186717, 'rougeL': 0.2798021796877333}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4680272987539452, 'rouge2': 0.14691618052617733, 'rougeL': 0.23678393774028422}\n","\n","\n","META:\n","\n","Binary Accuracy: 0.50  |  Mean Square Error: 22.56\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.49846719446047466, 'rouge2': 0.18695011373342413, 'rougeL': 0.32505385544325277}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.44546438318853127, 'rouge2': 0.13179367009196388, 'rougeL': 0.27606607959104484}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.45663704938185723, 'rouge2': 0.13272638374896073, 'rougeL': 0.22000374975851872}\n","\n","Binary Accuracy: 0.44  |  Mean Square Error: 20.28\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.4844332833267155, 'rouge2': 0.1803067710928415, 'rougeL': 0.28603763827249734}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.44343720240159956, 'rouge2': 0.13335975040567682, 'rougeL': 0.2757697250960148}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4704693226031723, 'rouge2': 0.13293609397051875, 'rougeL': 0.22312106297377088}\n","\n","MSFT:\n","\n","Binary Accuracy: 0.53  |  Mean Square Error: 14.21\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.4621148909857341, 'rouge2': 0.1338904227199656, 'rougeL': 0.264751121313342}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4560937621301571, 'rouge2': 0.1437937493137033, 'rougeL': 0.2869816972862205}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4682622751676134, 'rouge2': 0.14248295158646, 'rougeL': 0.22647684003418467}\n","\n","Binary Accuracy: 0.47  |  Mean Square Error: 23.32\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.489691890864076, 'rouge2': 0.16823755566963342, 'rougeL': 0.29298327890533055}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.45764808313262845, 'rouge2': 0.14415347752570182, 'rougeL': 0.2705659197016818}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4628886163042538, 'rouge2': 0.1342030970432304, 'rougeL': 0.2248523901142645}\n","\n","MU:\n","\n","\n","Binary Accuracy: 0.35  |  Mean Square Error: 44.35\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5163402321186589, 'rouge2': 0.18722459797175894, 'rougeL': 0.323849489256717}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4422813987886004, 'rouge2': 0.12595143521221225, 'rougeL': 0.2771466455786268}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.46706410901019557, 'rouge2': 0.13328113859337776, 'rougeL': 0.22406268422073966}\n","\n","Binary Accuracy: 0.56  |  Mean Square Error: 35.44\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5014003131243928, 'rouge2': 0.19306891913194849, 'rougeL': 0.31649364611368047}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.42695391163884566, 'rouge2': 0.12786531551459535, 'rougeL': 0.25611624346046086}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4770436933450599, 'rouge2': 0.13693796106748296, 'rougeL': 0.2290923200620776}\n","\n","\n","ZTS:\n","\n","Binary Accuracy: 0.68  |  Mean Square Error: 6.58\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5280181386829949, 'rouge2': 0.20774438211628393, 'rougeL': 0.32154185270560687}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.41529316328980187, 'rouge2': 0.12819144473828895, 'rougeL': 0.2695939146302464}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4592058663827326, 'rouge2': 0.1319873531137652, 'rougeL': 0.23848798470110574}\n","\n","Binary Accuracy: 0.59  |  Mean Square Error: 10.06\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.49493383304925753, 'rouge2': 0.18134200624860597, 'rougeL': 0.3192564704446295}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.40011403612586605, 'rouge2': 0.11489242051392032, 'rougeL': 0.25466683748879787}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.45014368744271793, 'rouge2': 0.13159929805395776, 'rougeL': 0.2307819283609618}\n","\n","SHW:\n","\n","Binary Accuracy: 0.53  |  Mean Square Error: 13.16\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5271904983502017, 'rouge2': 0.20949277077425862, 'rougeL': 0.3184654542302742}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4561185789055657, 'rouge2': 0.1464359095605757, 'rougeL': 0.26399484738722806}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4654701407966681, 'rouge2': 0.14201454951571524, 'rougeL': 0.24597224236317397}\n","\n","Binary Accuracy: 0.68  |  Mean Square Error: 9.26\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5390541710617475, 'rouge2': 0.2202892599278583, 'rougeL': 0.3299451360611247}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.41406470805593903, 'rouge2': 0.11649196546264166, 'rougeL': 0.24652163227644017}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4616846923767754, 'rouge2': 0.13528897366998457, 'rougeL': 0.23565409873684234}"],"metadata":{"id":"RZMiBhssgRco"}},{"cell_type":"code","source":["symbol_metrics"],"metadata":{"id":"eotTgq0cA3pB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["answers_base_amgn, answers_fine_tuned_amgn,gts_amgn = test_acc(test_dataset_AMGN)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UQuP0xh8pCI1","executionInfo":{"status":"ok","timestamp":1730606435158,"user_tz":240,"elapsed":1034537,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"724db789-cd0b-497d-ec5d-f923e0275af7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\rProcessing test samples:   0%|          | 0/19 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:   5%|▌         | 1/19 [00:41<12:27, 41.51s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  11%|█         | 2/19 [01:43<15:09, 53.50s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  16%|█▌        | 3/19 [02:36<14:10, 53.15s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  21%|██        | 4/19 [03:30<13:26, 53.75s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  26%|██▋       | 5/19 [04:20<12:13, 52.37s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  32%|███▏      | 6/19 [05:22<12:02, 55.61s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  37%|███▋      | 7/19 [06:16<10:59, 54.92s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  42%|████▏     | 8/19 [07:19<10:33, 57.55s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  47%|████▋     | 9/19 [08:05<09:00, 54.09s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  53%|█████▎    | 10/19 [08:52<07:45, 51.67s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  58%|█████▊    | 11/19 [09:44<06:55, 51.99s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  63%|██████▎   | 12/19 [10:53<06:38, 56.97s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  68%|██████▊   | 13/19 [11:39<05:22, 53.81s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  74%|███████▎  | 14/19 [12:35<04:32, 54.53s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  79%|███████▉  | 15/19 [13:28<03:35, 53.88s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  84%|████████▍ | 16/19 [14:21<02:41, 53.81s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  89%|████████▉ | 17/19 [15:26<01:53, 56.94s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples:  95%|█████████▍| 18/19 [16:23<00:57, 57.19s/it]Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n","Processing test samples: 100%|██████████| 19/19 [17:14<00:00, 54.45s/it]\n"]}]},{"cell_type":"markdown","source":["# 5.Print test results\n","\n","Here, three individual company datasets are used as examples. Note that the datasets used here are individual company datasets filtered using the filter function defined above. The function used to calculate the accuracy is the cacl_metrics function in utils.py. Another option is to calculate the accuracy using the acc calculation function defined above."],"metadata":{"id":"7ic3RJNAMTsi"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":829,"status":"ok","timestamp":1730608289333,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"},"user_tz":240},"id":"0Kz1naqWR4FR","outputId":"6bd9155c-f079-4bbd-f162-41f1a708e0a3"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Evaluating Base Model...\n","\n","Binary Accuracy: 0.58  |  Mean Square Error: 10.53\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5223651544398836, 'rouge2': 0.21362713277355666, 'rougeL': 0.33522895176684636}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4023204504978999, 'rouge2': 0.12352515463564404, 'rougeL': 0.25194228304506106}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.46768523659443323, 'rouge2': 0.1374051348274996, 'rougeL': 0.23872450947508786}\n","\n","Evaluating Fine-Tuned Model...\n","\n","Binary Accuracy: 0.44  |  Mean Square Error: 10.22\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5290888885650544, 'rouge2': 0.21237418298672206, 'rougeL': 0.31349996325313795}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.42372475951402305, 'rouge2': 0.11340730611523961, 'rougeL': 0.24092155525212838}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4679287192335615, 'rouge2': 0.1287125175625126, 'rougeL': 0.22998802901443488}\n","\n","Base Model Metrics:\n","{'valid_count': 19, 'bin_acc': 0.5789473684210527, 'mse': 10.526315789473685, 'pros_rouge_scores': {'rouge1': 0.5223651544398836, 'rouge2': 0.21362713277355666, 'rougeL': 0.33522895176684636}, 'cons_rouge_scores': {'rouge1': 0.4023204504978999, 'rouge2': 0.12352515463564404, 'rougeL': 0.25194228304506106}, 'anal_rouge_scores': {'rouge1': 0.46768523659443323, 'rouge2': 0.1374051348274996, 'rougeL': 0.23872450947508786}}\n","\n","Fine-Tuned Model Metrics:\n","{'valid_count': 18, 'bin_acc': 0.4444444444444444, 'mse': 10.222222222222221, 'pros_rouge_scores': {'rouge1': 0.5290888885650544, 'rouge2': 0.21237418298672206, 'rougeL': 0.31349996325313795}, 'cons_rouge_scores': {'rouge1': 0.42372475951402305, 'rouge2': 0.11340730611523961, 'rougeL': 0.24092155525212838}, 'anal_rouge_scores': {'rouge1': 0.4679287192335615, 'rouge2': 0.1287125175625126, 'rougeL': 0.22998802901443488}}\n"]}],"source":["\n","print(\"\\nEvaluating Base Model...\")\n","base_metrics = calc_metrics(answers_base_axp, gts_axp)\n","\n","print(\"\\nEvaluating Fine-Tuned Model...\")\n","fine_tuned_metrics = calc_metrics(answers_fine_tuned_axp, gts_axp)\n","\n","print(\"\\nBase Model Metrics:\")\n","print(base_metrics)\n","\n","print(\"\\nFine-Tuned Model Metrics:\")\n","print(fine_tuned_metrics)"]},{"cell_type":"code","source":["\n","print(\"\\nEvaluating Base Model...\")\n","base_metrics = calc_metrics(answers_base_aapl, gts_aapl)\n","\n","\n","print(\"\\nEvaluating Fine-Tuned Model...\")\n","fine_tuned_metrics = calc_metrics(answers_fine_tuned_aapl, gts_aapl)\n","\n","\n","print(\"\\nBase Model Metrics:\")\n","print(base_metrics)\n","\n","print(\"\\nFine-Tuned Model Metrics:\")\n","print(fine_tuned_metrics)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cc65tlwbmAlB","executionInfo":{"status":"ok","timestamp":1730608305223,"user_tz":240,"elapsed":856,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"09e3bf27-6936-4967-ac4a-3b3332e34a19"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Evaluating Base Model...\n","\n","Binary Accuracy: 0.67  |  Mean Square Error: 7.50\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5187885998413467, 'rouge2': 0.19345871742691625, 'rougeL': 0.30822545125472245}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.45678910561605257, 'rouge2': 0.1333132694798399, 'rougeL': 0.2718619761770612}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.47424456943014565, 'rouge2': 0.13376430906098646, 'rougeL': 0.24162801263470104}\n","\n","Evaluating Fine-Tuned Model...\n","\n","Binary Accuracy: 0.42  |  Mean Square Error: 10.42\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5157843287373985, 'rouge2': 0.18869514320087907, 'rougeL': 0.3051289829326338}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4723498461242249, 'rouge2': 0.17268287733224974, 'rougeL': 0.30508010786916423}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4878082023020895, 'rouge2': 0.15133388453068694, 'rougeL': 0.25018007882963167}\n","\n","Base Model Metrics:\n","{'valid_count': 18, 'bin_acc': 0.6666666666666666, 'mse': 7.5, 'pros_rouge_scores': {'rouge1': 0.5187885998413467, 'rouge2': 0.19345871742691625, 'rougeL': 0.30822545125472245}, 'cons_rouge_scores': {'rouge1': 0.45678910561605257, 'rouge2': 0.1333132694798399, 'rougeL': 0.2718619761770612}, 'anal_rouge_scores': {'rouge1': 0.47424456943014565, 'rouge2': 0.13376430906098646, 'rougeL': 0.24162801263470104}}\n","\n","Fine-Tuned Model Metrics:\n","{'valid_count': 19, 'bin_acc': 0.42105263157894735, 'mse': 10.421052631578947, 'pros_rouge_scores': {'rouge1': 0.5157843287373985, 'rouge2': 0.18869514320087907, 'rougeL': 0.3051289829326338}, 'cons_rouge_scores': {'rouge1': 0.4723498461242249, 'rouge2': 0.17268287733224974, 'rougeL': 0.30508010786916423}, 'anal_rouge_scores': {'rouge1': 0.4878082023020895, 'rouge2': 0.15133388453068694, 'rougeL': 0.25018007882963167}}\n"]}]},{"cell_type":"code","source":["\n","print(\"\\nEvaluating Base Model...\")\n","base_metrics = calc_metrics(answers_base_amgn, gts_amgn)\n","\n","\n","print(\"\\nEvaluating Fine-Tuned Model...\")\n","fine_tuned_metrics = calc_metrics(answers_fine_tuned_amgn, gts_amgn)\n","\n","\n","print(\"\\nBase Model Metrics:\")\n","print(base_metrics)\n","\n","print(\"\\nFine-Tuned Model Metrics:\")\n","print(fine_tuned_metrics)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pNRlpzjfuhZw","executionInfo":{"status":"ok","timestamp":1730608308559,"user_tz":240,"elapsed":869,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"00814b0e-778c-41b5-bb86-8f4d965e87d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Evaluating Base Model...\n","\n","Binary Accuracy: 0.32  |  Mean Square Error: 9.00\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.5059111306911762, 'rouge2': 0.17802353452066627, 'rougeL': 0.3149391449609365}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.40544265622054804, 'rouge2': 0.10849802495563123, 'rougeL': 0.25282588218903584}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.4572657735804972, 'rouge2': 0.1323204585199274, 'rougeL': 0.2435827300185122}\n","\n","Evaluating Fine-Tuned Model...\n","\n","Binary Accuracy: 0.32  |  Mean Square Error: 9.42\n","\n","Rouge Score of Positive Developments: {'rouge1': 0.49511856577750796, 'rouge2': 0.1501482456670449, 'rougeL': 0.28433235831688647}\n","\n","Rouge Score of Potential Concerns: {'rouge1': 0.4183051643018712, 'rouge2': 0.1321117418364849, 'rougeL': 0.24975751310768762}\n","\n","Rouge Score of Summary Analysis: {'rouge1': 0.46229185132370393, 'rouge2': 0.12473982348098471, 'rougeL': 0.24004280145405746}\n","\n","Base Model Metrics:\n","{'valid_count': 19, 'bin_acc': 0.3157894736842105, 'mse': 9.0, 'pros_rouge_scores': {'rouge1': 0.5059111306911762, 'rouge2': 0.17802353452066627, 'rougeL': 0.3149391449609365}, 'cons_rouge_scores': {'rouge1': 0.40544265622054804, 'rouge2': 0.10849802495563123, 'rougeL': 0.25282588218903584}, 'anal_rouge_scores': {'rouge1': 0.4572657735804972, 'rouge2': 0.1323204585199274, 'rougeL': 0.2435827300185122}}\n","\n","Fine-Tuned Model Metrics:\n","{'valid_count': 19, 'bin_acc': 0.3157894736842105, 'mse': 9.421052631578947, 'pros_rouge_scores': 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